基于脉冲耦合神经网络的混沌控制

根据脉冲耦合神经网络(PCNN)能产生混沌现象,研究了对配置混沌PCNN系统的李雅普诺夫指数使其稳定于期望点的方法。根据特定期望点的情况,按需要配置负的李雅普诺夫指数,产生不同的控制序列来改变混沌PCNN系统,达到稳定控制的要求。仿真和实验结果证明了该算法的有效性,实现了混沌PCNN系统从混沌状态到稳定期望点的控制。

Chaos control based on pulse-coupled neural networks

According to that the Pulse Coupling Neural Network (PCNN) can display chaotic phenomenon under certain condition, the method of configuring Lyapunov exponents of chaotic PCNN system to set the system converged to a fixed point was studied. In this method, Lyapunov exponents were configured to be negative, according to the expectations of a specific point, the corresponding control sequences were produced, which altered the chaotic system so as to achieve the goal of stable control. Simulation results demonstrate the effectiveness of the proposed method, and the chaotic PCNN systems can be controlled from chaotic state to stable expectation.